Data Engineer

• Data Architecture & Pipeline Development: Design, build, and maintain robust data pipelines and architectures to support private equity investment workflows. Ensure seamless ingestion, transformation, and integration of large, diverse datasets (financial, ESG, market, and alternative data) from multiple internal and external sources.
• Scalability, Performance & Reliability: Develop scalable data infrastructure, monitor and optimize data flow performance, reliability, and data quality. Ensure compliance with security, privacy, and governance standards.
• Collaboration with Data Science & Investment Teams: Partner with data scientists, investment professionals, and operations teams to operationalize analytics. Enable efficient data access, versioning, and reproducibility across the AI and analytics platform.
• Automation & Continuous Improvement: Automate data workflows, implement CI/CD pipelines, and contribute to the continuous improvement of data engineering practices. Evaluate new tools and architectures to enhance efficiency, scalability, and cost-effectiveness.

This role contributes to a groundbreaking AI platform that transforms investment analysis in private equity, enhancing data-driven decision-making and accelerating due diligence processes.

• Education: Master’s degree in Data Engineering, Computer Science, or a related technical field.
• Experience: 3–5 years of hands-on experience designing and maintaining large-scale data pipelines. Proven experience working with structured and unstructured financial data or other complex data domains. Experience collaborating in finance, consulting, or private equity environments is a must.
• Skills: Advanced proficiency in Python and SQL for data processing and transformation. Hands-on experience with cloud data platforms (e.g., AWS, Azure, GCP, or Databricks) and data workflow orchestration tools (e.g., Airflow, Prefect, or Dagster). Strong understanding of data modeling, schema design, and API integration. Experience with big data frameworks (Spark, Kafka, Delta Lake, etc.) is highly desirable. Familiarity with version control (Git) and DevOps practices (CI/CD pipelines). Strong communication skills and ability to work cross-functionally with non-technical stakeholders. Fluency in English; French is a plus.

Flexibility: Team members can work remotely and have the opportunity to work with colleagues around the world.

Work Environment: Multicultural environment with teams in the US, France, and Tunisia.

Career Development: Rapid growth creates ongoing opportunities for personal and professional growth, allowing you to shape the company’s culture and evolution.

Professional Development: Work alongside industry-leading experts and gain exposure to advanced AI and ML technologies applied in private equity.

Mentorship & Training: Structured training and mentorship, access to educational platforms, and knowledge-sharing sessions.

Global Perspective: Collaborate with teams across Europe and the United States, gaining international experience.

Transparency: Monthly “Ask Me Anything” meetings, frequent business/finance updates, and strategy-sharing discussions.

Well-being: Focus on being a learning organization that values teamwork, thoughtfulness, and work/life balance.

SESAMm extracts behavioral insights from millions of unstructured text data sources across the public internet in over 100 languages, helping clients distill valuable intelligence on businesses and their customers to make investment decisions and manage risk. They work with firms in private equity, investment banking, insurance, and asset management to apply Natural Language Processing and AI algorithms for e-reputation, competitive positioning, sentiment analysis, risk, and ESG factors.